The new C-Suite ally: Generative AI

Traditionally, strategic decisions relied heavily on historical data analysis, market research, and the collective experience and intuition of leadership teams. While these remain valuable, GenAI introduces a new dimension – the ability to generate novel insights, simulate potential outcomes, and identify previously unseen patterns within vast datasets. This shift is empowering CxOs to move beyond reactive problem-solving towards proactive strategy formulation.  

One of the most significant ways CxOs are leveraging GenAI is in scenario planning and risk assessment. By feeding GenAI models with diverse data points – from economic indicators and geopolitical events to competitor activities and internal performance metrics – leaders can generate a multitude of potential future scenarios.

This allows them to stress-test their strategies against various possibilities, identify potential risks and opportunities, and develop robust contingency plans. Imagine a Chief Financial Officer using GenAI to simulate the impact of interest rate hikes on the company's profitability under different market conditions, enabling them to proactively adjust investment strategies and mitigate potential losses.  

GenAI is proving invaluable in market analysis and identifying white spaces. CxOs can utilize these tools to analyze complex market trends, understand evolving customer needs, and pinpoint unmet demands. By generating novel product or service concepts based on these insights, GenAI can help organizations identify and capitalize on emerging opportunities before their competitors. A Chief Marketing Officer, for instance, could use GenAI to analyze social media trends and customer reviews to identify unmet needs in a specific product category, leading to the development of innovative offerings that capture new market segments.  

Competitive intelligence is another area where Generative-AI is providing a significant edge. By analyzing publicly available data, financial reports, patents, and even news articles, GenAI can generate comprehensive profiles of competitors, identify their strategic moves, and predict their potential future actions. This allows CxOs to anticipate competitive threats and formulate proactive strategies to maintain or gain market share. A Chief Strategy Officer might employ GenAI to analyze a competitor's recent investments and product launches to anticipate their next strategic direction and adjust the company's own roadmap accordingly.

Beyond external analysis, GenAI is enhancing internal decision-making processes. By analyzing internal data such as sales figures, operational metrics, and employee feedback, GenAI can identify areas for improvement, optimize resource allocation, and even predict potential bottlenecks. This data-driven approach empowers CxOs to make more informed decisions about operational efficiency and organizational structure. A Chief Operating Officer could use GenAI to analyze production data and identify inefficiencies in the supply chain, leading to optimized processes and reduced costs.  

It is assisting CxOs in communication and stakeholder engagement. These tools can generate compelling narratives, presentations, and reports based on complex data analysis, making it easier for leaders to communicate their vision and strategies to internal and external stakeholders. This can be particularly useful in investor relations, where clear and data-backed communication is crucial for building trust and securing funding. A CEO might leverage GenAI to create a compelling investor presentation that clearly articulates the company's strategic direction and growth potential based on AI-driven market insights.  

However, the integration of GenAI into strategic decision-making is not without its challenges. CxOs need to be mindful of data privacy and security concerns, ensure the ethical use of AI, and develop robust governance frameworks to oversee its deployment. Furthermore, the "black box" nature of some GenAI models requires leaders to develop a critical understanding of the underlying logic and potential biases to ensure the reliability and trustworthiness of the generated insights

From data-driven to GenAI-augmented decisions

In the past, decision-making at the C-level heavily relied on structured data analytics: BI tools, CRM dashboards, financial projections. While powerful, these tools often required significant human interpretation to extract actionable insights.

Now, GenAI models are trained on vast internal and external datasets — from customer feedback to market signals — and can synthesize information in ways that mimic human reasoning. Instead of simply asking "what happened," CxOs can now ask "what could happen next" and "what's the smartest course of action?"

At IndiaMART InterMESH Limited, the integration of AI into decision-making is already reshaping how strategies are crafted.

"At IndiaMART, AI is not just integral to our current operations but vital for strategic foresight," said Nikhil Prabhakar, Chief Information Officer, IndiaMART.

"Our extensive history of utilizing AI for discovery, matchmaking, and conversational commerce positions us well to leverage it for future possibilities. This includes reading customer behaviour and preferences on the platform, thereby helping managers to make the platform more personalized, intuitive, and responsive as per the specific needs of the user."

Prabhakar added that AI’s role extends to predicting shifts in buyer and supplier behavior, simulating the impact of new initiatives, and uncovering untapped opportunities.

Similarly, at Mastercard, GenAI is becoming indispensable to securing the financial ecosystem and enhancing operational foresight.

"At Mastercard, we see GenAI as a powerful enabler—augmenting rather than replacing human decision-making," said Rajesh Chopra, Senior Vice President and Head, Advisors, South Asia.

"GenAI helps us unlock data-driven insights, model complex scenarios, and simulate outcomes faster than ever before. While strategic visioning and values remain inherently human, GenAI enhances the ability to act with precision and foresight."

Mastercard’s use of AI through platforms like Brighterion and RiskRecon showcases how real-time fraud detection, risk management, and customer personalization are increasingly underpinned by GenAI systems.

Unlocking the power of unstructured data

While traditional GenAI applications focus on structured datasets, a significant frontier remains largely untapped — the vast swathes of unstructured "dark data" sitting in contracts, credit memos, regulatory reports, and risk assessments.

Aashish Mehta, Founder and CEO of nRoad, emphasizes this critical gap.
"Most strategic decisions rely on data, but the reality is that a lot of that data sits in unstructured formats," he explained. nRoad’s platform, CONVUS, addresses this by transforming unstructured content into structured, contextual insights.

"It does not just extract data but understands patterns, recognizes risk, and organizes information the way a domain expert would," Mehta added.
CONVUS further minimizes hallucinations by anchoring outputs in statistical validation, giving executives insights they can trust.

The real-world impact is already visible.

"For one large financial services client, processing thousands of siloed credit and risk documents manually was a bottleneck," Mehta shared.

"With CONVUS, we automated the extraction of key entities and risk flags. What used to take days now takes hours — enabling faster credit decisioning, reduced compliance risk, and transforming leadership engagement with risk data."

Mehta firmly believes that domain-specific context is crucial to GenAI’s relevance at the executive level:
"Generic AI often falls short because it lacks domain intelligence. When you are making high-stakes decisions, you need insights that are contextual, traceable, and backed by data, not just generated text."

GenAI in financial services

Financial services, with their high regulatory standards and data sensitivity, present both a challenge and an opportunity for GenAI deployment. Broadridge India exemplifies how GenAI can be leveraged responsibly at scale.

"Broadridge is strategically leveraging Generative AI to strengthen decision-making in risk management, compliance, and client intelligence through four key pillars: Products and Services, Productivity, Platform, and Governance," said Kishore Seshagiri, Chief Digital Officer, Broadridge India.

At the forefront of this initiative is OpsGPT®, Broadridge’s GenAI-powered solution designed for the T+1 post-trade environment. OpsGPT enables natural language interactions with complex, multi-asset trade data—transforming raw inputs into real-time insights, visualizations, and summaries.
"This empowers teams to detect anomalies, anticipate risks, and meet compliance requirements faster and more effectively," Seshagiri noted.

Beyond risk management, OpsGPT automates time-intensive compliance tasks, offers multilingual capabilities, and eliminates the need for coding through intuitive design. Importantly, Broadridge has embedded a robust governance framework around all AI initiatives, ensuring security, regulatory compliance, and transparency.

Trustworthiness is central to Broadridge’s approach.

"We adopt a multi-layered governance framework grounded in data protection, informed consent, model accuracy, and regulatory compliance," Seshagiri explained.
Human-in-the-loop validation, use of curated data sources, and avoidance of LLM hallucinations are built into platforms like OpsGPT and BondGPT to maintain the highest standards of trust and explainability.

One notable success story lies in settlement fail management.

"By integrating GenAI into post-trade workflows, we automated the research, classification, and resolution of settlement fails—leading to up to a 50% improvement in operational efficiency within select T+1-impacted areas," Seshagiri shared.

Executives now have real-time dashboards offering deep insights into Key Risk Indicators (KRIs) and trade lifecycle events, significantly enhancing strategic agility.

To ensure scalability and compliance in regulated environments, Broadridge focuses on secure architecture, verified data use, built-in compliance layers, real-time data timeliness, and continuous retraining—making GenAI not just a tool but a trusted operational foundation.

Emerging Use Cases: Strategy, Risk, and Innovation

CxOs are deploying GenAI across several strategic fronts:

Business Strategy Formulation: GenAI models simulate various market scenarios, helping leaders stress-test strategies against potential disruptions.

Risk Management: Generative models can draft risk matrices based on real-time data, providing earlier warnings of emerging threats.

Customer Experience Innovation: Executives are using AI to identify unmet customer needs and new growth opportunities faster than traditional market research would allow.

Companies like Mastercard are using GenAI for predictive fraud detection and supply chain resilience. IndiaMART is enhancing personalization and engagement on its platform. Meanwhile, firms like Broadridge and nRoad are pioneering trusted, domain-specific GenAI applications in financial services.

Challenges: Trust, Bias, and Governance

Despite the enthusiasm, CxOs remain cautious about overreliance on GenAI outputs. Concerns around model bias, data hallucination, and explainability persist. Many leaders are putting guardrails in place: enforcing human-in-the-loop systems, regular model audits, and ethical AI use policies.

Mastercard’s structured approach to responsible AI, Broadridge’s four-pillar governance framework, and nRoad’s domain-first design all showcase how trust and validation remain the foundation of GenAI for leadership decision-making.

"It’s your ally, not a rival or a future substitute!" Chopra emphasized, underscoring the principle of augmenting — not replacing — human judgment.

The Next Frontier: Personalized Executive Copilots

Some forward-looking companies are experimenting with personalized GenAI copilots — AI agents tailored to the needs and style of individual executives. These copilots can learn a leader’s priorities, communication style, and strategic preferences over time, offering increasingly personalized advice.

This is not a vision for the distant future. As models grow more sophisticated and multi-modal (integrating text, speech, images, and even video), CxOs may soon interact with AI "advisors" as naturally as they do with their human teams.

Conclusion

Generative AI is rapidly becoming an indispensable tool for CxOs in navigating the complexities of the modern business environment. Its ability to analyze vast datasets, generate novel scenarios, and provide predictive insights is empowering leaders to make more informed, proactive, and ultimately, more successful strategic decisions. As the technology continues to evolve, CxOs who embrace and strategically integrate GenAI into their decision-making processes will be best positioned to lead their organizations towards a future of innovation and growth. The revolution in the C-suite is underway, and GenAI is undoubtedly one of its most powerful catalysts.